Quoted from: https://calliope.readthedocs.io/en/stable/index.html 

Calliope focuses on flexibility, high spatial and temporal resolution, the ability to execute many runs based on the same base model, and a clear separation of framework (code) and model (data). Its primary focus is on planning energy systems at scales ranging from urban districts to entire continents. In an optional operational mode it can also test a pre-defined system under different operational conditions. Calliope’s built-in tools allow interactive exploration of results:

A model based on Calliope consists of a collection of text files (in YAML and CSV formats) that define the technologies, locations and resource potentials. Calliope takes these files, constructs an optimisation problem, solves it, and reports results in the form of xarray Datasets which in turn can easily be converted into Pandas data structures, for easy analysis with Calliope’s built-in tools or the standard Python data analysis stack.

Calliope is developed in the open on GitHub and contributions are very welcome (see the Development guide).

Key features of Calliope include:

• Model specification in an easy-to-read and machine-processable YAML format

• Generic technology definition allows modelling any mix of production, storage and consumption

• Resolved in space: define locations with individual resource potentials

• Resolved in time: read time series with arbitrary resolution

• Able to run on high-performance computing (HPC) clusters

• Uses a state-of-the-art Python toolchain based on Pyomo, xarray, and Pandas

• Freely available under the Apache 2.0 license